A S Albahri, Z T Al-Qaysi, Laith Alzubaidi, Alhamzah Alnoor, O S Albahri, A H Alamoodi, Anizah Abu Bakar
{"title":"在基于稳态视觉诱发电位的脑机接口应用中使用深度学习技术的系统性综述:当前趋势与未来信任方法》。","authors":"A S Albahri, Z T Al-Qaysi, Laith Alzubaidi, Alhamzah Alnoor, O S Albahri, A H Alamoodi, Anizah Abu Bakar","doi":"10.1155/2023/7741735","DOIUrl":null,"url":null,"abstract":"<p><p>The significance of deep learning techniques in relation to steady-state visually evoked potential- (SSVEP-) based brain-computer interface (BCI) applications is assessed through a systematic review. Three reliable databases, PubMed, ScienceDirect, and IEEE, were considered to gather relevant scientific and theoretical articles. Initially, 125 papers were found between 2010 and 2021 related to this integrated research field. After the filtering process, only 30 articles were identified and classified into five categories based on their type of deep learning methods. The first category, convolutional neural network (CNN), accounts for 70% (<i>n</i> = 21/30). The second category, recurrent neural network (RNN), accounts for 10% (<i>n</i> = 3/30). The third and fourth categories, deep neural network (DNN) and long short-term memory (LSTM), account for 6% (<i>n</i> = 30). The fifth category, restricted Boltzmann machine (RBM), accounts for 3% (<i>n</i> = 1/30). The literature's findings in terms of the main aspects identified in existing applications of deep learning pattern recognition techniques in SSVEP-based BCI, such as feature extraction, classification, activation functions, validation methods, and achieved classification accuracies, are examined. A comprehensive mapping analysis was also conducted, which identified six categories. Current challenges of ensuring trustworthy deep learning in SSVEP-based BCI applications were discussed, and recommendations were provided to researchers and developers. The study critically reviews the current unsolved issues of SSVEP-based BCI applications in terms of development challenges based on deep learning techniques and selection challenges based on multicriteria decision-making (MCDM). A trust proposal solution is presented with three methodology phases for evaluating and benchmarking SSVEP-based BCI applications using fuzzy decision-making techniques. Valuable insights and recommendations for researchers and developers in the SSVEP-based BCI and deep learning are provided.</p>","PeriodicalId":45630,"journal":{"name":"International Journal of Telemedicine and Applications","volume":null,"pages":null},"PeriodicalIF":3.1000,"publicationDate":"2023-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10164869/pdf/","citationCount":"0","resultStr":"{\"title\":\"A Systematic Review of Using Deep Learning Technology in the Steady-State Visually Evoked Potential-Based Brain-Computer Interface Applications: Current Trends and Future Trust Methodology.\",\"authors\":\"A S Albahri, Z T Al-Qaysi, Laith Alzubaidi, Alhamzah Alnoor, O S Albahri, A H Alamoodi, Anizah Abu Bakar\",\"doi\":\"10.1155/2023/7741735\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>The significance of deep learning techniques in relation to steady-state visually evoked potential- (SSVEP-) based brain-computer interface (BCI) applications is assessed through a systematic review. Three reliable databases, PubMed, ScienceDirect, and IEEE, were considered to gather relevant scientific and theoretical articles. Initially, 125 papers were found between 2010 and 2021 related to this integrated research field. After the filtering process, only 30 articles were identified and classified into five categories based on their type of deep learning methods. The first category, convolutional neural network (CNN), accounts for 70% (<i>n</i> = 21/30). The second category, recurrent neural network (RNN), accounts for 10% (<i>n</i> = 3/30). The third and fourth categories, deep neural network (DNN) and long short-term memory (LSTM), account for 6% (<i>n</i> = 30). The fifth category, restricted Boltzmann machine (RBM), accounts for 3% (<i>n</i> = 1/30). The literature's findings in terms of the main aspects identified in existing applications of deep learning pattern recognition techniques in SSVEP-based BCI, such as feature extraction, classification, activation functions, validation methods, and achieved classification accuracies, are examined. A comprehensive mapping analysis was also conducted, which identified six categories. Current challenges of ensuring trustworthy deep learning in SSVEP-based BCI applications were discussed, and recommendations were provided to researchers and developers. The study critically reviews the current unsolved issues of SSVEP-based BCI applications in terms of development challenges based on deep learning techniques and selection challenges based on multicriteria decision-making (MCDM). A trust proposal solution is presented with three methodology phases for evaluating and benchmarking SSVEP-based BCI applications using fuzzy decision-making techniques. 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A Systematic Review of Using Deep Learning Technology in the Steady-State Visually Evoked Potential-Based Brain-Computer Interface Applications: Current Trends and Future Trust Methodology.
The significance of deep learning techniques in relation to steady-state visually evoked potential- (SSVEP-) based brain-computer interface (BCI) applications is assessed through a systematic review. Three reliable databases, PubMed, ScienceDirect, and IEEE, were considered to gather relevant scientific and theoretical articles. Initially, 125 papers were found between 2010 and 2021 related to this integrated research field. After the filtering process, only 30 articles were identified and classified into five categories based on their type of deep learning methods. The first category, convolutional neural network (CNN), accounts for 70% (n = 21/30). The second category, recurrent neural network (RNN), accounts for 10% (n = 3/30). The third and fourth categories, deep neural network (DNN) and long short-term memory (LSTM), account for 6% (n = 30). The fifth category, restricted Boltzmann machine (RBM), accounts for 3% (n = 1/30). The literature's findings in terms of the main aspects identified in existing applications of deep learning pattern recognition techniques in SSVEP-based BCI, such as feature extraction, classification, activation functions, validation methods, and achieved classification accuracies, are examined. A comprehensive mapping analysis was also conducted, which identified six categories. Current challenges of ensuring trustworthy deep learning in SSVEP-based BCI applications were discussed, and recommendations were provided to researchers and developers. The study critically reviews the current unsolved issues of SSVEP-based BCI applications in terms of development challenges based on deep learning techniques and selection challenges based on multicriteria decision-making (MCDM). A trust proposal solution is presented with three methodology phases for evaluating and benchmarking SSVEP-based BCI applications using fuzzy decision-making techniques. Valuable insights and recommendations for researchers and developers in the SSVEP-based BCI and deep learning are provided.
期刊介绍:
The overall aim of the International Journal of Telemedicine and Applications is to bring together science and applications of medical practice and medical care at a distance as well as their supporting technologies such as, computing, communications, and networking technologies with emphasis on telemedicine techniques and telemedicine applications. It is directed at practicing engineers, academic researchers, as well as doctors, nurses, etc. Telemedicine is an information technology that enables doctors to perform medical consultations, diagnoses, and treatments, as well as medical education, away from patients. For example, doctors can remotely examine patients via remote viewing monitors and sound devices, and/or sampling physiological data using telecommunication. Telemedicine technology is applied to areas of emergency healthcare, videoconsulting, telecardiology, telepathology, teledermatology, teleophthalmology, teleoncology, telepsychiatry, teledentistry, etc. International Journal of Telemedicine and Applications will highlight the continued growth and new challenges in telemedicine, applications, and their supporting technologies, for both application development and basic research. Papers should emphasize original results or case studies relating to the theory and/or applications of telemedicine. Tutorial papers, especially those emphasizing multidisciplinary views of telemedicine, are also welcome. International Journal of Telemedicine and Applications employs a paperless, electronic submission and evaluation system to promote a rapid turnaround in the peer-review process.